In order to test whether large receptive fields help integrating long-range features, we evaluated CycleNet on the Pathfinder challenge (Fig.REF b).
This task is inspired by the study of human visual perception, and requires tracking paths over long distances to distinguish whether two white circles are connected.
Deep convolutional networks struggle on this task (e.g. ResNet50), because of their limited ability of integrating features over long distances [1]}.
Since CycleNet integrates all pixels after one cycle, we predict that it should perform well on this task.
[1]} proposes a biologically-inspired horizontal gated recurrent neural network that performs ~\(100\%\)  on the task, but their model is hard to compare with standard CNNs.
